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cdec cfg 'test/example/cdec.ini'
feature: WordPenalty (no config parameters)
State is 0 bytes for feature WordPenalty
feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz')
Loading the LM will be faster if you build a binary file.
Reading test/example/nc-wmt11.en.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581)
State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz
feature: RuleIdentityFeatures (no config parameters)
State is 0 bytes for feature RuleIdentityFeatures
feature: RuleNgramFeatures (no config parameters)
State is 0 bytes for feature RuleNgramFeatures
feature: RuleShape (no config parameters)
Example feature: Shape_S00000_T00000
State is 0 bytes for feature RuleShape
Seeding random number sequence to 380245307
dtrain
Parameters:
k 100
N 4
T 3
scorer 'stupid_bleu'
sample from 'kbest'
filter 'uniq'
learning rate 0.0001
gamma 0
pairs 'XYX'
hi lo 0.1
pair threshold 0
select weights 'VOID'
l1 reg 0 'none'
cdec cfg 'test/example/cdec.ini'
input 'test/example/nc-wmt11.1k.gz'
output '-'
stop_after 20
(a dot represents 10 inputs)
Iteration #1 of 3.
.. 20
Stopping after 20 input sentences.
WEIGHTS
Glue = -0.1015
WordPenalty = -0.0152
LanguageModel = +0.21493
LanguageModel_OOV = -0.3257
PhraseModel_0 = -0.050844
PhraseModel_1 = +0.25074
PhraseModel_2 = +0.27944
PhraseModel_3 = -0.038384
PhraseModel_4 = -0.12041
PhraseModel_5 = +0.1047
PhraseModel_6 = -0.1289
PassThrough = -0.3094
---
1best avg score: 0.17508 (+0.17508)
1best avg model score: -1.2392 (-1.2392)
avg # pairs: 1329.8
avg # rank err: 649.1
avg # margin viol: 677.5
non0 feature count: 874
avg list sz: 88.6
avg f count: 85.643
(time 0.25 min, 0.75 s/S)
Iteration #2 of 3.
.. 20
WEIGHTS
Glue = -0.0792
WordPenalty = -0.056198
LanguageModel = +0.31038
LanguageModel_OOV = -0.4011
PhraseModel_0 = +0.072188
PhraseModel_1 = +0.11473
PhraseModel_2 = +0.049774
PhraseModel_3 = -0.18448
PhraseModel_4 = -0.12092
PhraseModel_5 = +0.1599
PhraseModel_6 = -0.0606
PassThrough = -0.3848
---
1best avg score: 0.24015 (+0.065075)
1best avg model score: -10.131 (-8.8914)
avg # pairs: 1324.7
avg # rank err: 558.65
avg # margin viol: 752.85
non0 feature count: 1236
avg list sz: 84.9
avg f count: 88.306
(time 0.22 min, 0.65 s/S)
Iteration #3 of 3.
.. 20
WEIGHTS
Glue = -0.051
WordPenalty = -0.077956
LanguageModel = +0.33699
LanguageModel_OOV = -0.4726
PhraseModel_0 = +0.040228
PhraseModel_1 = +0.18
PhraseModel_2 = +0.15618
PhraseModel_3 = -0.098908
PhraseModel_4 = -0.036555
PhraseModel_5 = +0.1619
PhraseModel_6 = +0.0078
PassThrough = -0.4563
---
1best avg score: 0.25527 (+0.015113)
1best avg model score: -13.906 (-3.7756)
avg # pairs: 1356.3
avg # rank err: 562.1
avg # margin viol: 757.35
non0 feature count: 1482
avg list sz: 86.65
avg f count: 87.475
(time 0.23 min, 0.7 s/S)
Writing weights file to '-' ...
done
---
Best iteration: 3 [SCORE 'stupid_bleu'=0.25527].
This took 0.7 min.
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